Scheme for arithmetic operations in finite field and group operations over elliptic curves realizing improved computational speed

ABSTRACT

A scheme for arithmetic operations in finite field and group operations over elliptic curves capable of realizing a very fast implementation. According to this scheme, by using a normal basis [α α+1], the multiplicative inverse calculation and the multiplication in the finite field GF(2 2n ) can be realized as combinations of multiplications, additions and a multiplicative inverse calculation in the subfield GF(2 n ). Also, by using a standard basis [1 α], the multiplication, the square calculation, and the multiplicative inverse calculation in the finite field GF(2 2n ) can be realized as combinations of multiplications, additions and a multiplicative inverse calculation in the subfield GF(2 n ). These arithmetic operations can be utilized for calculating rational expressions expressing group operations over elliptic curves that are used in information security techniques such as elliptic curve cryptosystems.

CROSS-REFERENCE TO RELATED APPLICATION

This is a divisional of U.S. application Ser. No. 09/484,896 filed Jan. 18, 2000, which in turn is a divisional of U.S. application Ser. No. 09/014,891 filed Jan. 28, 1998 and now U.S. Pat. No. 6,038,581.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a scheme for arithmetic operations in finite field and group operations over elliptic curves, and more particularly, to a computational scheme for arithmetic operations in finite fields such as GF(2^(m)) which is to be utilized in realizing error correction coding (such as algebraic geometric coding) and information security technique (such as elliptic curve cryptosystem) including key distribution and authentication using group operations over elliptic curves.

2. Description of the Background Art

As a fast implementation of multiplicative inverse calculation in GF(2^(m)), a scheme based on multiplication using a normal basis has been proposed by J. L. Massey and J. K. Omura (see U.S. Pat. No. 4,587,627). This scheme is based on the principle that, when the Fermat's little theorem over finite fields holds in a form of:

x ^(2m−1)=1 for an element x (≠0) of GF(2^(m))

it is possible to calculate multiplicative inverse in a form of:

x ⁻¹ =x ^(2m−2)

Other schemes based on this same principle are also disclosed, for example, in Agnew et al.: “Arithmetic Operations in GF(2^(m))”, Journal of Cryptology, Vol. 6, pp. 3-13, 1993, and P. C. van Oorschot, S. A. Vanstone: “A Geometric Approach to Root Finding in GF(q^(m)), IEEE Transactions of Information Theory, Vol. 35, No. 2, pp. 444-453, March 1989.

Either scheme utilizes the fact that multiplication in GF(2^(m)) can be efficiently realized by hardware by using a normal basis, and realizes multiplicative inverse calculation in GF(2^(m)) as a combination of multiplication and shift (including rotate) operations in GF(2^(m)). In the presently known algorithm, it is known that multiplications in GF(2^(m)) are required for [log₂m]+{the number of 1 in the binary representation of (m−1)}−1 times, bit shift operations are required for (m−1) times, and when GF(2^(m)) is a quadratic extension of GF(2^(m/2)), by the use of subfield in multiplicative inverse calculation in GF(2^(m)), two multiplications in GF(2^(m)) and one shift operation in GF(2^(m/2)) constitute one multiplicative inverse calculation in GF(2^(m/2)).

However, when this multiplication algorithm is straightforwardly implemented by software, there arises a problem of lowering of efficiency because of tedious bit unit handling.

For this reason, there is a known scheme for calculating multiplication in GF(2^(m)) by using subfield (see A. Pincin: “A New Algorithm for Multiplication in Finite Fields”, IEEE Transactions on Computers, Vol. 38, No. 7, pp. 1045-1049, July 1989, for example).

In the case of realizing finite field arithmetic by software, because of the looser constraint on memory size compared with the case of hardware implementation, the fast implementation becomes possible by providing a table of calculation results obtained by preliminary calculations and reading out necessary information from the table subsequently. A very fast algorithm utilizing this fact is disclosed in E. De Win et al.: “A Fast Software Implementation for Arithmetic Operations in GF(2^(n))”, Advances in Cryptology—ASIACRYPT'96, Lecture Notes in Computer Science 1163, pp. 65-76, Springer-Verlag, 1996, for example.

Now, many secret key cryptosystems improve their security by iterating F functions several times. It is known that the security can be guaranteed by utilizing exponential calculations in F function (see K. Nyberg: “Differentially Uniform Mappings for Cryptography”, Advances in Cryptology—EUROCRYPT'93, Lecture Notes in Computer Science 765, pp. 55-64, Springer-Verlag, 1994, and K. Nyberg, L. R. Knudsen: “Provable Security Against a Differential Attack”, Journal of Cryptology, Vol. 8, pp. 27-37, 1995). In these references, it is recommended to construct F function by using cube calculations or multiplicative inverse calculations.

However, when conventionally used input data are represented by using a normal basis on prime field GF(2) and multiplicative inverse calculation in GF(2^(2n)) is straightforwardly implemented by software using the algorithm of van Oorschot et al., there arises a problem of lowering of efficiency because of tedious bit unit handling.

Now, elements of a group E(K) of elliptic curves over a field K can be expressed in terms of either homogeneous coordinates formed by a set of three elements of K or affine coordinates formed by a set of two elements of K. Addition of E(K) can be calculated by arithmetic operations over field K in ether expression using homogeneous coordinates or affine coordinates.

In constructing a device for realizing group operations over elliptic curves, a field K can be chosen be a finite field GF(q), and in particular, a finite field GF(2^(n)) with characteristic 2 is often employed because it is possible to realize a fast implementation.

Among arithmetic operations over finite field, the very fast implementation is possible for addition and additive inverse by the conventional implementation scheme, but considerable time is required for calculating multiplication and multiplicative inverse (hereafter inverse refers to multiplicative inverse unless otherwise indicated). Consequently, a time required for addition of groups over elliptic curves can be evaluated by the required number of multiplication and inverse calculations over field K.

On the other hand, conventionally, inverse calculation over finite field with characteristic 2 requires an enormous amount of calculations compared with multiplication. For this reason, the conventional schemes for implementing group operations over elliptic curves are mainly the implementation using homogeneous coordinates which does not require inverse calculations, even though the required number of multiplication calculations becomes rather large (see A. J. Menczes, S. A. Vanstone: “Elliptic Curve Cryptosystems and Their Implementation”, Journal of Cryptology, Vol. 6, pp. 209-289, 1993, for example).

However, in recent years, a scheme for implementing inverse calculation in finite field with characteristic 2 has been developed, and schemes using affine coordinates for expressing elements of group over elliptic curves have been proposed, for example, in E. De Win et al.: “A Fast Software Implementation for Arithmetic Operations in GF(2^(n))”, Advances in Cryptology—ASIACRYPT'96, Lecture Notes in Computer Science 1163, pp. 65-76, Springer-Verlag, 1996. In the following, this scheme will be referred to as De Win's scheme.

Outline of the implementation of finite field according to the De Win's scheme is as follows. When a number of bits for basic operations of a processor is w (8 or 16, for example), all the operations over ground field are calculated in advance by using GF(2^(w)) as ground field. Also, using an odd degree three term irreducible polynomial over GF(2) in a form of:

x ^(d) +x ^(t)+1(d>t),

operations in GF(2wd) are represented as:

GF(2^(wd))≅GF(2w)[x]/(x ^(d) +x ^(t)+1)

where a symbol ≅ denotes isomorphism of fields (see S. MacLane, G. Birkhoff: “Algebra”, Chelsea Publishing, 1967, for detail), and then using this representation, E(GF(2^(wd)) is implemented. In the De Win's scheme, inverse calculation in finite field utilizes the extended Euclidean algorithm over GF(2^(w)) which is the general inverse calculation method, and many multiplications and divisions are required in executing the extended Euclidean algorithm.

Note that finite fields with characteristic 2 are important because they have data structures suitable for computers, and they can be utilized in error correction coding and cryptography. Individual element of a finite field GF(2^(n)) can be represented by using n-th degree irreducible polynomial f(X) over GF(2) as:

GF(2^(n))≅GF(2)[x]/(f(x))

so that it can be represented by polynomial of (n−1)-th degree or less. In other words, by regarding coefficients GF(2) of polynomial as bits, GF(2^(n)) can be represented in terms of n bits.

When such a representation is used, addition can be represented by exclusive OR of n bits (note that subtraction is the same as addition in the case of field with characteristic 2) so that it can be implemented easily and efficiently. As for the implementation of multiplication and division, there are known schemes which are more efficient than the straightforward scheme for calculating a product of (n−1)-th degree polynomials and then calculating a residue of f(X).

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a scheme for calculating multiplicative inverse and multiplication which realizes efficient multiplicative inverse calculation in GF(2^(2n)), by storing preliminary calculation results in a table in view of the fact that more memory capacities are available for software implementation compared with hardware implementation.

It is another object of the present invention to provide a scheme for arithmetic operations in finite field and group operations over elliptic curves capable of realizing a very fast implementation by using an optimal normal basis.

According to one aspect of the present invention there is provided a method for calculating a multiplicative inverse in finite field GF(2^(2n)), comprising the steps of: expressing an element m∈GF(2^(2n)) as

m=xα+y(α+1) (x, y∈GF(2^(n)))

where α∈GF(2^(2n))\GF(2^(n)), α2+α+a=0, and a∈GF(2^(n)) so that a multiplicative inverse m⁻¹ of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications, additions and a multiplicative inverse calculation in subfield GF(2^(n)) given by

m ⁻¹=(a(x+y)² +xy)⁻¹ yα+(a(x+y)² +xy)⁻¹ x(α+1)

by combining a normal basis [α α+1] with extended Euclidean algorithm; and calculating the multiplicative inverse m⁻¹ of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications, additions and a multiplicative inverse calculation in the subfield GF(2^(n)).

According to another aspect of the present invention there is provided a method for calculating a multiplication in finite field GF(2^(2n)), comprising the steps of: reducing a multiplication of two elements m₁ and m₂ in GF(2^(2n)) into multiplications and additions in subfield GF(2^(n)) by expressing m₁, m₂∈GF(2^(2n)) as

m ₁ =x ₁ α+y ₁(α+1), m ₂ =x ₂ α+y ₂(α+1)

where x_(i), y_(i)∈GF(2^(n)), i=1, 2, α∈GF(2^(2n))\GF(2^(n)), α²+α+a=0, and α∈GF(2^(n)) so that a multiplication m_() of m1, m₂∈GF(2^(2n)) is given by

m _() =m ₁ m ₂=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x _(i) +y _(i))(x ₂ +y ₂))(a+1);

and calculating the multiplication m_(θ) by executing said multiplications and additions in the subfield GF(2^(n)).

According to another aspect of the present invention there is provided a method for calculating a rational expression in finite field GF(2^(2n)) in a form of: $\frac{p\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}{q\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}$

where p, q∈GF(2^(2n))[X₁, X₂, . . . , X_(r)], comprising the steps of: calculating a multiplication in GF(2^(2n)) that arises in calculations using polynomials p, q∈GF(2^(2n))[X₁, X₂, . . . , X_(r)] according to a multiplication formula:

(x ₁ α+y ₁(α+1))×(x ₂ α+y ₂(α+1))

=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

where x₁, x₂, y₁, y₂, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α)); calculating an inverse q⁻¹ of q in GF(2^(2n)) according to an inverse calculation formula:

(xα+y(α+1))⁻¹

=(a(x+y)² +xy)⁻¹ (yα+x(α+1))

where x, y, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+α=0 and GF(2^(2n))=GF(2^(n))(α)); and calculating p×q⁻¹ by using said multiplication formula.

According to another aspect of the present invention there is provided a method for calculating a multiplication in finite field GF(2^(2n)), comprising the steps of: expressing elements m₁, m₂∈GF(2^(2n))≅GF(2^(n))[x]/(x²+x+a) as

m ₁ =x ₁ +y ₁α

m ₂ =x ₂ +y ₂α(x ₁ , x ₂ , y ₁ , y ₂ ∈GF(2^(n)))

where α∉GF(2^(n)), α₂+α+a=0, and a∈GF(2^(n)) so that a multiplication m₁ m₂ of the elements m₁ and m₂ in the finite field GF(2^(2n)) is expressed as a combination of multiplications and additions in subfield GF(2^(n)) given by

m ₁ m ₂=(x ₁ x ₂ +ay ₁ y ₂)+((x ₁ +y ₁)(x ₂ +y ₂)+x ₁ x ₂)α

by using a standard basis [1 α]; and calculating the multiplication m₁ m₂ of the elements m₁ and m₂ in the finite field GF(2^(2n)) by executing said combination of multiplications and additions in the subfield GF(2^(n)).

According to another aspect of the present invention there is provided a method for calculating a square in finite field GF(2^(2n)), comprising the steps of: expressing an element m∈GF(2^(2n))≅GF(2^(n))[x]/(x²+x+a) as

m=x+yα(x, y∈GF(2^(n)))

where α∉GF(2^(n)), α²+α+a=0, and a∈GF(2^(n)) so that a square ma of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications and additions in subfield GF(2^(n)) given by

m ²=(x ² +ay ²)+y ²α

by using a standard basis [1 α]; and calculating the square m² of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications and additions in the subfield GF(2^(n)).

According to another aspect of the present invention there is provided a method for calculating a multiplicative inverse in finite field GF(2^(2n)), comprising the steps of: expressing an element m∈GF(2^(2n))≅GF(2^(n))[x]/(x²+x+a) as

m=x+yα(x, y∈GF(2^(n)))

where α∉GF(2^(n)), α²+α+a=0, and a∈GF(2^(n)) so that a multiplicative inverse m⁻¹ of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications, additions and a multiplicative inverse calculation in subfield GF(2^(n)) given by

m ⁻¹=(x(x+y)+ay ²)⁻¹((x+y)+yα)

by using a standard basis [1 α]; and calculating the multiplicative inverse m⁻¹ of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications, additions and a multiplicative inverse calculation in the subfield GF(2^(n)).

According to another aspect of the present invention there is provided a method for calculating a rational expression in finite field GF(2^(2n)) in a form of: $\frac{p\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}{q\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}$

where p, q∈GF(2^(2n))[X₁, X₂, . . . , X_(r)], comprising the steps of: calculating a multiplication in GF(2^(2n)) that arises in calculations using polynomials p, q∈GF(2^(2n))[X₁, X₂, . . . , X_(r)] according to a multiplication formula:

(x ₁ +y ₁α)×(x ₂ +y ₂α)

=(x ₁ x ₂ +ay ₁ y ₂)+((x ₁ +y ₁)(x ₂ +y ₂)+x ₁ x ₂)α

and a square calculation formula

(x ₁ +y ₁α)²=(x ₁ ² +ay ₁ ²)+y ₁ ²α

where x₁, x₂, y₁, y₂, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α); calculating an inverse q⁻¹ of q in GF(2^(2n)) according to an inverse calculation formula:

(x ₁ +y ₁α)⁻¹=(x ₁(x ₁ +y ₁)+ay ₁ ²)⁻¹((x ₁ +y ₁)+y ₁α)

where x₁, y₁, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α); and calculating p×q⁻¹ by using said multiplication formula.

These methods can be implemented in forms of corresponding devices or articles of manufacture.

Other features and advantages of the present invention will become apparent from the following description taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a 2n bit inverse calculation device according to the first embodiment of the present invention.

FIG. 2 is a block diagram of an n bit multiplication unit that can be used in the 2n bit inverse calculation device FIG. 1.

FIG. 3 is a block diagram of an n bit inverse calculation unit that can be used in the 2n bit inverse calculation device of FIG. 1.

FIG. 4 is a block diagram of another n bit multiplication unit that can be used in the 2n bit inverse calculation device of FIG. 1.

FIG. 5 is a block diagram of another n bit multiplication unit that can be used in the 2n bit inverse calculation device of FIG. 1.

FIG. 6 is a block diagram of a 2n bit multiplication device according to the first embodiment of the present invention.

FIG. 7 is a block diagram of a GF(2^(e)) addition device according to the second embodiment of the present invention.

FIG. 8 is a block diagram of a GF(2^(e)) multiplication device according to the second embodiment of the present inventing

FIG. 9 is a block diagram of a GF(2^(e)) inverse calculation device according to the second embodiment of the present invention.

FIG. 10 is a block diagram of a GF(2^(2n)) addition device according to the second embodiment of the present invention.

FIG. 11 is a block diagram of a GF(2^(2n)) multiplication device according to the second embodiment of the present invention.

FIG. 12 is a block diagram of a GF(2^(2n)) square calculation device according to the second embodiment of the present invention.

FIG. 13 is a block diagram of a GF(2^(2n)) inverse calculation device according to the second embodiment of the present invention.

FIG. 14 is a table showing comparison of performances by the first and second embodiments according to the present invention.

FIG. 15 is a block diagram of an elliptic curve group inverse calculation device according to the third embodiment of the present invention.

FIG. 16 is a block diagram of an elliptic curve group addition device according to the third embodiment of the present invention.

FIG. 17 is a block diagram of an elliptic curve group comparison device according to the third embodiment of the present invention.

FIG. 18 is a block diagram of an elliptic curve group special addition device according to the third embodiment of the present invention.

FIG. 19 is a block diagram of an elliptic curve group double calculation device according to the third embodiment of the present invention.

FIG. 20 is a block diagram of an elliptic curve group special double calculation device according to the third embodiment of the present invention.

FIG. 21 is a block diagram of an elliptic curve group natural number multiple calculation device according to the third embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to FIG. 1 to FIG. 6, the first embodiment of a scheme for calculating multiplicative inverse and multiplication in finite field according to the present invention will be described in detail. In the following description, inverse refers to multiplicative inverse unless otherwise Indicated.

This first embodiment is directed to a scheme for calculating an inverse in finite field GF(2^(2n)) by using arithmetic operations in GF(2^(n)). This first embodiment is based on the fact that GF(2^(2n)) is a two-dimensional vector space in GF(2^(n)) so that by using:

α∈GF(2^(2n))\GF(2^(n)), α² +α+a=0, a∈GF(2^(n))

where a symbol\denotes a difference set, an element m∈GF(2^(2n)) can be expressed as:

m=xα+y(α+1)(x, y∈GF(2^(n)))

Then, when an element m∈GF(2^(2n)) is expressed by:

m=xα+y(α+1)(x, y∈GF(2^(n)))

an inverse of m in GF(2^(2n)) can be calculated by utilizing the extended Euclidean algorithm as:

(a(x+y)² +xy)⁻¹ yα+(a(x+y)² +xy)⁻¹ x(α+1)

Therefore, the inverse calculation requires two addition calculations in GF(2^(n)) (namely t₇=x+y=t₃+t₅ and t₁₅=a(x+y)²+xy=t₁₁+t₁₄ in a configuration of FIG. 1)), five multiplication calculations (namely t_(1)=(x+y)²=t₈×t₉, t₁₁=a(x+y)²=at_(1), t₁₄=xy=t₁×t₁₂, t₁₉=t₁₇×t₄, and t_(2)=t₁₃×t₁₈ in a configuration of FIG. 1), and one inverse calculation in GF(2^(n)) (namely t₁₆=t₁₅ ⁻¹ in a configuration of FIG. 1).

FIG. 1 shows a configuration of a 2n bit inverse calculation device according to this first embodiment. This 2n bit inverse calculation device of FIG. 1 comprises two n bit exclusive OR units (add) 106 and 112, five n bit multiplication units (mul) 103, 107, 110, 111 and 113, and one n bit inverse calculation unit (inv) 114, which are configured to carry out the following processing.

STEP 1: A 2n bit input m is split into two n bit parts x and y.

STEP 2: x is duplicated to yield t₁ and t₂, and t₁ is set as an input to the n bit multiplication unit 103.

STEP 3: t₂ is duplicated to yield t₃ and t₄, and t₃ is set as an input to the n bit exclusive OR unit 106 while t is set as an input to the n bit multiplication unit 107.

STEP 4: y is duplicated to yield t₅ and t₆, and t₅ is set as an input to the n bit exclusive OR unit 106.

STEP 5: An exclusive OR t₇ of the inputs t₃ and t₅ is calculated by the n bit exclusive OR unit 106.

STEP 6: t₇ is duplicated to yield t₈ and t₉, and t₈ and t₉ are set as inputs to the n bit multiplication unit 110.

STEP 7: A product t_(1) of the inputs t₈ and t₉ is calculated by the n bit multiplication unit 110, and set as an input to the n bit multiplication unit 111.

STEP 8: A product t₁₁ of a constant a and the input t_(1) is calculated by the n bit multiplication unit 111, and, set as an input to the n bit exclusive OR unit 112.

STEP 9: t₆ is duplicated to yield t₁₂ and t₁₃, and t₁₂ is set as an input to the n bit multiplication unit 103 while t₁₃ is set as an input to the n bit multiplication unit 113.

STEP 10: A product t₁₄ of the inputs t₁₁ and t₁₂ is calculated by the n bit multiplication unit 103, and set as an input to the n bit exclusive OR unit 112.

STEP 11: An exclusive OR t₁₅ of the inputs t₁₁ and t₁₄ is calculated by the n bit exclusive OR unit 112, and set as an input to the n bit inverse calculation unit 114.

STEP 12: An inverse t₁₆ of the input t₁₅ is calculated by the n bit inverse calculation unit 114.

STEP 13: t₁₆ is duplicated to yield t₁₇ and t₁₈, and t₁₇ is set as an input to the n bit multiplication unit 107 while tie is set as an input to the n bit multiplication unit 113.

STEP 14: A product t₁₉ of the inputs t₄ and t₁₇ is calculated by the n bit multiplication unit 107.

STEP 15: A product t_(2) of the inputs t₁₃ and tie is calculated by the n bit multiplication unit 113.

STEP 16: t₁₉ and t_(2) are joined and outputted as an inverse m⁻¹ of the input m (where the output result is in 2n bits).

Note that the n bit inverse calculation unit 114 can be realized by recursively regarding t₁₅ as m in this configuration of FIG. 1, if it cannot be implemented in a form of FIG. 3 described below because of the limitation on cache memory.

Note also that, in this configuration of FIG. 1, t: and t₄ are the same value (x), so that there is no need to look up logarithmic conversion table to be described below in the n bit multiplication units 103 and 107 twice. By using the value obtained by the n bit multiplication unit 103 directly in the n bit multiplication unit 107, it is possible to reduce a number of times for looking up the logarithmic conversion table. The similar remarks also apply to t₈ and t₉ pair, t₁₂ and t₁₃ pair, and t₁₇ and t₁₈ pair.

FIG. 2 shows one exemplary configuration of the n bit multiplication unit that can be used in the configuration of FIG. 1.

In this n bit multiplication unit 200, in carrying out multiplication in GF(2^(n))^(x), where GF(2^(n))^(x) denotes the multiplicative group of GF(2^(n)), when a logarithmic conversion table and an exponential conversion table can be stored in a memory, a logarithmic conversion table 202 and an exponential conversion table 205 are provided as shown in FIG. 2.

Then, for two elements x₁ and x₂ in GF(2^(n))^(x) entered by a query unit 201, corresponding two logarithms e₁=log_(g)x₁ and e₂=log_(g) x₂ are obtained by looking up the logarithmic conversion table 202, and e=e₁+e₂ mod(2^(n)−1) is calculated by an addition unit 203. Then, for this e entered by a query unit 204, an exponential g^(e) is obtained by looking up the exponential conversion table 205, so as to obtain a product x=x₁×x₂. Here, an element g is set to be a primitive root GF(2^(n))^(x)=GF(2^(n))\{0} in GF(2^(n))′.

More specifically, this n bit multiplication unit 200 of FIG. 2 carries out the following processing.

STEP 1: The query unit 201 enters two inputs x₁ and x₂ into the logarithmic conversion table 202.

STEP 2: Two logarithms e_(i)=log_(g)x₁ and e₂=log_(g)x₂ are obtained by using the logarithmic conversion table 202, returned to the query unit 201.

STEP 3: The query unit 201 enters e₁ and e₂ into the addition unit 203.

STEP 4: The addition unit 203 calculates e=e₁+e₂ mod(2^(n)−1), and enters it into the query unit 204.

STEP 5: The query unit 204 enters e into the exponential conversion table 205.

STEP 6: The exponential x=g^(e) is obtained by using the exponential conversion table 205, and returned to the query unit 204.

STEP 7: The query unit 204 outputs x as the product of x₁ and x₂.

FIG. 3 shows an exemplary configuration of the n bit inverse calculation unit that can be used in the configuration of FIG. 1.

In this n bit inverse calculation unit 300, the inverse calculation in GF(2^(n))^(x) is carried out as follows. Namely, for an element x in GF(2^(n))^(x) entered by a query unit 301, a logarithm e=log_(g)x is obtained by looking up a logarithmic conversion table 302, and f=−e mod(2^(n) 'l) is calculated by a complement calculation unit 303. Then, for this f entered by a query unit 304, an exponential g^(f) is obtained by looking up an exponential conversion table 305, so as to obtain an inverse x′=x⁻¹. Note that the logarithmic conversion table 202 and the exponential conversion table 205 of FIG. 2 can be directly utilized as the logarithmic conversion table 302 and the exponential conversion table 305 of FIG. 3.

More specifically, this n bit inverse calculation unit 300 of FIG. 3 carries out the following processing.

STEP 1: The query unit 301 enters an input x into the logarithmic conversion table 302.

STEP 2: The logarithm e=log_(g)x is obtained by using the logarithmic conversion table 302, and returned to the query unit 301.

STEP 3: The query unit 301 enters e into the complement calculation unit 303.

STEP 4: The complement calculation unit 303 calculates f=−e mod(2^(n)−1), and enters it into the query unit 304.

STEP 5: The query unit 304 enters f into the exponential conversion table 305.

STEP 6: The exponential x′=g^(f) is obtained by using the exponential conversion table 305, and returned to the query unit 304.

STEP 7: The query unit 304 outputs x′ as the inverse of x.

FIG. 4 shows another exemplary configuration of the n bit multiplication unit that can be used in the configuration of FIG. 1.

In this n bit multiplication unit 400, in carrying out multiplication in GF(2^(n)) when a multiplication table can be stored in a memory, a multiplication table 402 is provided as shown in FIG. 4.

Then, for two elements x₁ and x₂ in GF(2^(n)) entered by a query unit 401, a corresponding multiplication result x=x₁×x₂ is obtained by looking up the multiplication table 402.

More specifically, this n bit multiplication unit 400 of FIG. 4 carries out the following processing.

STEP 1: The query unit 401 enters two inputs x₁ and x₂ into the multiplication table 402.

STEP 2: The multiplication result x=x₁×x₂ is obtained by using the multiplication table 402, and returned to the query unit 401.

STEP 3: The query unit 401 outputs x as the product of x₁ and x₂.

FIG. 5 shows another exemplary configuration of the n bit multiplication unit that can be used in the configuration of FIG. 1.

This n bit multiplication unit 500 is for a case where one of multiplying number (x₁=a) is constant, as in the n bit multiplication unit 111 of FIG. 1. Consequently, a multiplication table 502 for x=a×x₂ (or a×x₂×x₃) is provided as shown in FIG. 5.

Then, for another multiplying number x₂ entered by a query unit 501, a corresponding multiplication result x=a×x₂ is obtained by looking up the multiplication table 502. Note that this n bit multiplication unit 500 can be similarly used for a case where a number of multiplying numbers is increased, as in a case of obtaining ax₁x₂.

More specifically, this n bit multiplication unit 500 of FIG. 5 carries out the following processing.

STEP 1: The query unit 501 enters an input x into the multiplication table 502.

STEP 2: The multiplication result x′=a×x is obtained by using the multiplication table 502, and returned to the query unit 501.

STEP 3: The query unit 501 outputs x′ as the product of a and x.

Now, when the n bit multiplication unit cannot be implemented in any of the configurations of FIG. 2, FIG. 4 and FIG. 5 described above because of the limitation on cache memory, it is possible to implement the n bit multiplication unit from n/2 bit multiplication units as follows. Note that this implementation can be used recursively.

Namely, the multiplication in arbitrary 2n bits can be reduced to multiplications in n bits and additions in n bits. When n is sufficiently small, the n bit multiplication can be realized by the fast implementation of any of FIG. 2, FIG. 4 and FIG. 5 described above, so that the following description is given in terms of a parameter n.

For two elements m₁, m₂∈GF(2^(2n)) expressed by:

m ₁ =x ₁ α+y ₁(α+1),

m ₂ =x ₂ α+y ₂(α+1), (x _(i) , y _(i) , ∈GF(2^(n)), i=1, 2),

a product m_() of m₁ and m₂ can be expressed as:

m _() =m ₁ m ₂=(x ₁ x _(2+a)(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

Thus the multiplication in GF(2^(n)) can be reduced to the arithmetic operations in its subfield, and the multiplication table or the logarithmic conversion table and the exponential conversion table required for the arithmetic operations in the subfield can be realized in smaller size.

FIG. 6 shows an exemplary configuration of a 2n bit multiplication device according to this first embodiment. This 2n bit multiplication device of FIG. 6 comprises four n bit exclusive OR units (add) 605, 609, 614 and 615, and three n bit multiplication units (mul) 604, 607 and 611l which are configured to carry out the following processing.

STEP 1: A 2n bit input m₁ is split into two n bit parts x₁ and y₁.

STEP 2: A 2n bit input m₂ is split into two n bit parts x₂ and y₂.

STEP 3: x₁ is duplicated to yield t₁ and t₂, and t₁ is set as an input to the n bit multiplication unit 604 while t₂ is set as an input to the n bit exclusive OR unit 605.

STEP 4: y₁ is duplicated to yield t₃ and t₄, and t₃ is set as an input to the n bit exclusive OR unit 605 while t₄ is set as an input to the n bit multiplication unit 607.

STEP 5: y₂ is duplicated to yield t₅ and t₆, and t₅ is set as an input to the n bit exclusive OR unit 609 while t is set as an input to the n bit multiplication unit 607.

STEP 6: x₂ is duplicated to yield t₇ and t₈, and t₇ is set as an input to the n bit exclusive OR unit 609 while t₈ is set as an input to the n bit multiplication unit 604.

STEP 7: An exclusive OR t₉ of the inputs t₅ and t₇ is calculated by the n bit exclusive OR unit 609, and set as an input to the n bit multiplication unit 611.

STEP 8: An exclusive OR t_(1) of the inputs t₂ and t₃ is calculated by the n bit exclusive OR unit 605, and set as an input to the n bit multiplication unit 611.

STEP 9: A product t₁₂ of two inputs t₉ and t_(1) and a constant a is calculated by the n bit multiplication unit 611. Here, the n bit multiplication unit 611 can be in a configuration of FIG. 5 described above.

STEP 10: A product t₁₃ of the inputs t₁ and t₈ is calculated by the n bit multiplication unit 604, and set as an input to the n bit exclusive OR unit 614.

STEP 11: A product t₁₄ of the inputs t₄ and t₆ is calculated by the n bit multiplication unit 607, and set as an input to the n bit exclusive OR unit 615.

STEP 12: t₁₂ is duplicated to yield t₁₅ and t₁₆, and t₁₅ is set as an input to the n bit exclusive OR unit 614 while t₁₆ is set as an input to the n bit exclusive OR unit 615.

STEP 13: An exclusive OR t₁₇ of the inputs t₁₃ and t₁₅ is calculated by the n bit exclusive OR unit 614.

STEP 14: An exclusive OR t₁₈ of the inputs t₁₄ and t₁₆ is calculated by the n bit exclusive OR unit 615.

STEP 15: t₁₇ and t₁₈ are Joined and outputted as a product me of the inputs m₁ and m₂.

Thus it can be seen that the product of elements In GF(2^(2n)) can be calculated by four multiplications and four additions in GF(2^(n)).

Note that this reduction of 2n bit multiplication to n bit multiplications and additions can be applied to each 2n bit multiplication appearing in the 2n bit inverse calculation of FIG. 1 described above.

Note also that the above noted fact that, when an element mGF(2^(2n)) is expressed by:

m=xα+y(α+1)(x, y∈GF(2^(n)))

an inverse m⁻¹ of m in GF(2^(2n)) can be expressed by:

m⁻¹=(a(x +y)² +xy)⁻¹ yα+(a(x+y)² +xy)⁻¹ x(α+1

can be demonstrated as follows (assuming that m≠0).

Namely, for two elements m₁, m₂GF(2^(2n)) expressed by:

m ₁ =x ₁ α+y ₁(α+1),

m ₂ =x ₂ α+y ₂(α+1), (x _(i) , y _(i) ∈GF(2^(n)), i=1, 2),

a product m_() of m₁ and m₂ can be expressed as:

m _() =m ₁ m ₂=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

and therefore it follows that: $\begin{matrix} {{m \times m^{- 1}} = \quad {{\left\{ {\frac{xy}{{a\left( {x + y} \right)}^{2} + {xy}} + {{a\left( {x + y} \right)}\quad \frac{xy}{{a\left( {x + y} \right)}^{2} + {xy}}}} \right\} \alpha} +}} \\ {\quad {\left\{ {\frac{xy}{{a\left( {x + y} \right)}^{2} + {xy}} + {{a\left( {x + y} \right)}\quad \frac{x + y}{{a\left( {x + y} \right)}^{2} + {xy}}}} \right\} \left( {\alpha + 1} \right)}} \\ {= \quad {{\alpha + \left( {\alpha + 1} \right)} = 1}} \end{matrix}$

Now, the algorithm of this first embodiment is compared with a combination of conventional algorithms. Namely, by using the algorithm of Agnew et al. mentioned above, the inverse calculation in GF(2^(2n)) can be reduced to multiplications in GF(2^(2n)) and inverse calculation and shift operation in GF(2^(n)). That is,

Agnew:

one inverse calculation in GF(2^(n))

two multiplications in GF(2^(2n))

one shift operation in GF(2^(n))

Then, when the multiplication in GF(2^(2n)) is realized by arithmetic operations in subfield GF(2^(n)) by using the scheme of Pincin mentioned above, it requires four multiplications and four additions in GF(2^(n)). Consequently,

Agnew+Pincin:

one inverse calculation in GF(2^(n))

eight multiplications in GF(2^(n))

eight additions in GF(2^(n))

one shift operation in GF(2^(n))

In contrast, the required arithmetic operations in the first embodiment are as follows.

First Embodiment:

one inverse calculation in GF(2^(n))

five multiplications in GF(2^(n))

two additions in GF(2^(n))

one shift operation in GF(2^(n))

Thus it can be seen that the first embodiment can save three multiplications in GF(2^(n)) and six additions in GF(2^(n)).

Next, an amount of calculations in an application to cryptographic processing will be described.

For F function used in the encryption processing of 64 bit block cipher, the use of cube calculation and inverse calculation is recommended. For an exemplary case of 64 bit cipher, a case of realizing the cube calculation by the scheme of Pincin and a case of realizing the inverse calculation by the first embodiment can be compared as follows.

Cube Calculation: (Pincin)

seven multiplications in GF(2¹⁶)

three additions in GF(2¹⁶)

Inverse Calculation: (First Embodiment)

one inverse calculation in GF(2¹⁶)

five multiplications in GF(2¹⁶)

two additions in GF(2¹⁶)

Consider a case of implementation on workstation. Most of the present-day CPUs have a cache memory in size of 256 KB or more, so that the logarithmic conversion table and the exponential conversion table for subfield GF(2¹⁶) can be realized as fast accessible tables.

When the required number of times for looking up table and calculation contents for e and f in the multiplication and the inverse calculation described above, it can be seen that the inverse calculation can be realized faster than the multiplication because the inverse calculation is a monomial operation. Thus it can be seen that, in comparison with the cube calculation, the first embodiment can save one multiplication or more in GF(2¹⁶) and one addition in GF(2¹⁶)

As described, according to this first embodiment, the normal basis and the extended Euclidean algorithm are combined to reduce the inverse calculation in GF(2^(2n)) to multiplications, additions, and an inverse calculation in GF(2^(n)), so as to reduce a required number of multiplications and additions in subfield GF(2^(n)) compared with the conventional scheme.

Also, according to this first embodiment, binomial operation (multiplication) in subfield GF(2^(n))^(x) is converted into binomial operation (addition) in Z/(2^(n)−1)Z (additive cyclic group of order (2^(n)−1)) where calculation using monomial operation (logarithmic conversion) is easier, and this calculation result is re-converted into monomial operation (exponential conversion) in subfield GF(2^(n)). Here, in order to provide a multiplication table in GF(2^(n)) for use in binomial operations, a memory of (2^(n))²×n bits will be required, but the logarithmic conversion table and the exponential conversion table storing preliminary calculation results for monomial operations of the logarithmic conversion and the exponential conversion will be required to have a size of about 2^(n)×n bits each.

Also, according to this first embodiment, when a sufficient amount of fast read accessible memory is available, a multiplication table storing calculation results for binomical operations (multiplications) in GF(2^(n)) can be provided, so as to reduce a processing load required for table look up and calculation in Z/(2^(n)−1)Z.

Also, according to this first embodiment, when one of the numbers to be multiplied together in multiplication is fixed (x₁=a), a multiplication table storing calculation results for multiplications (x=a×x₂ or x=a×x₂×x₃) in GF(2^(n)) can be provided.

Also, according to this first embodiment, multiplication in GF(2^(n)) is reduced to arithmetic operations in subfield GF(2^(n/2)). A table for storing multiplication result in GF(2^(n)) requires about (2^(n))²×n bits, but a table for storing multiplication result in GF(2^(n/2)) requires about (2^(n/2))²×(n/2)=(2^(n))×(n/2) bits. Consequently, by iterating the reduction of multiplication into multiplication in subfield, it becomes possible to utilize a subfield that has a parameter n for which a table size can be reduced to that of a fast read accessible memory (cache memory).

Also, according to this first embodiment, when the logarithmic conversion table and the exponential conversion table cannot be stored because of a limited size of cache memory, multiplication in GF(2^(n)) is reduced to multiplication in GF(2^(n/2)) and a calculation algorithm in that subfield is applied. Similarly, when an inverse calculation in GF(2^(n)) cannot be executed, it is reduced to multiplications and an inverse calculation in GF(2^(n/2)) and a calculation algorithm in that subfield is applied.

Also, according to this first embodiment, the number of table accesses is reduced by storing values read out from tables for one calculation operation and utilizing them in other calculation operations.

It is to be noted that, according to the above described scheme of the first embodiment, when a finite field GF(2^(2/n)) is represented by:

GF(2^(2n))≈GF(2^(n))[x]/(x ² +x+a)(a∈GF(2^(n)))

and a normal basis [α α+1] is taken as a basis when GF(2^(2n)) is regarded as a two-dimensional vector space in GF(2^(n) where α is a root of x²+x+a=0, multiplication in GF(2^(2n)) can be calculated by arithmetic operations in GF(2^(n)) because of the following equation.

(x ₁ α+y ₁(α+1))×(x ₂ α+y ₂(α+1))

=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

Similarly, the square can be calculated by the following equation:

(xα+y(α+1))²

(x ² +a(x ² +y ²))α+(y ² +a(x ² +y ₂))(α+1)

while the inverse can be calculated by the following equation:

(xα+y(α+1))⁻¹

=(a(x+y)² +xy)⁻¹(yα+x(α+1))

According to this scheme of the first embodiment, when n is divisible by a large number (such as 16 or more), it becomes possible to obtain an inverse in finite field GF(2^(n)) more efficiently than the De Win's scheme mentioned above. As a consequence, it becomes possible to realize a faster implementation of group operations over elliptic curves than the De Win's scheme by reducing a required number of multiplications and divisions.

Also, according to this first embodiment, when elements other than a point at infinity 0 of a group over elliptic curves E(GF(2^(2n))) in finite field GF(2^(2n)) are expressed in terms of affine coordinates (x, y), group operations over elliptic curves can be expressed as rational expressions in x and y, and it is possible to calculate a rational expression in a form of: $\frac{p\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}{q\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}$

where p, q∈GF(2^(2n))[X₁, X₂, . . . ,X_(r)] by calculating a multiplication in GF(2^(2n)) that arises in calculations using polynomials p, q E∈GF(2^(2n))[X₁, X₂, . . . , X_(r)] according to:

(x ₁ α+y ₁(α+1))×(x ₂ α+y ₂(α+1))

=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

where x₁, x₂, y₁, y₂, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α);

and calculating an inverse q⁻¹ of q in GF(2^(2n)) according to:

(xα+y(α+1))⁻¹

=(a(x+y)² +xy)⁻¹(ya+x(α+1))

where x, y, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α);

and then calculating p×q⁻¹ by repeating the same multiplication as described above for calculations using polynomials p and q.

Referring now to FIG. 7 to FIG. 14, the second embodiment of a scheme for arithmetic operations in finite field according to the present invention will be described in detail.

The first embodiment described above is based on the fact that, when a finite field GF(2^(2n)) is represented by:

GF(2^(2n))≅GF(2n)[x]/(x ² +x+a)(a∈GF(2^(n)))

and a normal basis [α α+1] is taken as a basis when GF(2^(2n))

is regarded as a two-dimensional vector space in GF(2^(n)) where α is a root of x²+x+a=0, multiplication in GF(2^(2n)) can be calculated by arithmetic operations in GF(2^(n)) because of the following equation.

(x ₁ α+y ₁(α+1))×(x ₂ α+y ₂(α+1))

=(x ₁ x ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))α

+(y ₁ y ₂ +a(x ₁ +y ₁)(x ₂ +y ₂))(α+1)

Similarly, the square can be calculated by the following equation:

(xα+y(α+1))²

=(x ² +a(x ² +y ²))α+(y ² +a(x ² +y ²))(α+1)

while the inverse can be calculated by the following equation:

(xα+y(α+1))⁻¹

=(a(x+y)² +xy)⁻¹(yα+x(α+1))

so that operations in GF(2^(2n)) can be calculated by using arithmetic operations in GF(2^(n)).

This first embodiment realizes the fast implementation by utilizing successive quadratic extensions of finite field. However, there is no evidence that a basis taken in realizing quadratic extensions is an optimal one. In this second embodiment, the faster implementation is realized by taking a basis different from the normal basis used in the first embodiment. That is, this second embodiment realizes the faster implementation by using a standard basis instead of the normal basis of the first embodiment.

More specifically, in this second embodiment, when a finite field GF(2^(2n)) is represented by:

GF(2^(2n))≅GF(2^(n))[x]/(x ² +x+a)(a∈GF(2^(n)))

and a standard basis [1 α] is taken as a basis when GF(2^(2n)) is regarded as a two-dimensional vector space in GF(2^(n)) where α is a root of x²+x+a=0 arithmetic operations can be calculated as follows.

Addition:

(x ₁ +y ₁α)+(x ₂ +y ₂α)=(x ₁ +x ₂)+(y ₁ +y ₂)α  (1)

Multiplication:

(₁ +y ₁α)×(x ₂ +y ₂α)

=(x ₁ x ₂ +ay ₁ y ₂)+((x ₁ +y ₁)(x ₂ +y ₂ +x ₁ x ₂)α  (2)

Square:

(x ₁ +y ₁α)²=(x ₁ ² +ay ₁ ²)α+y ₁ ²α  (3)

Inverse:

(x ₁ +y ₁α)⁻¹=(x ₁(x ₁ +y ₁)+ay ₁ ²)⁻¹(x ₁ +y ₁)+y ₁α  (4)

Now this second embodiment will be described in further detail with references to the drawings.

A finite field GF(2^(e2) ^(t) ) has a subfield GF(2^(e2) ^(t−1) ), and can be regarded as a vector space in GF(2^(e2) ^(t−1) ). From this fact it follows that operations in GF(2^(e2) ^(t) ) can be reduced to operations in GF(2^(e2) ^(t−1) ), and similarly, they can be reduced to operations in GF(2^(e2) ^(t−2) ), . . . , GF(2^(e)), as explicitly noted above.

In further detail, in this second embodiment, operations in GF(2^(e2) ^(t) ) are reduced to operations in GF(2^(e2) ^(t−1) ), operations in GF(2_(e2) ^(t−1) ) are reduced to operations in GF(2^(e2) ^(t) ), and so on so forth sequentially, until operations are finally reduced to those in GF(2^(e)). Then, arithmetic operations in GF(2^(e)) are realized as follows.

FIG. 7 shows a configuration of a GF(2^(e)) addition device 20 to be used for arithmetic operations in finite field according to this second embodiment. This GF(2^(e)) addition device 20 comprises an e bit exclusive OR (XOR) unit 20 a, and operates as follows.

STEP 101: An exclusive OR value x+(XOR) y of inputs x and y are calculated by the e bit exclusive OR unit 20 a.

STEP 102: An output x+(XOR) y of the e bit exclusive OR unit 20 a is outputted as x+y.

FIG. 8 shows a configuration of a GF(2^(e)) multiplication device 21 to be used for arithmetic operations in finite field according to this second embodiment. This GF(2^(e)) multiplication device 21 comprises a query unit 21 a and a multiplication table 21 b, which are configured to operate as follows.

STEP 201: The query unit 21 a looks up the multiplication table 21 b by using inputs x and y as retrieval key so as to retrieve a product x×y of the inputs x and y.

STEP 202: The query unit 21 a outputs a value x×y obtained from the multiplication table 21 b.

As for a GF(2^(e)) square calculation to be used for arithmetic operations in finite field according to this second embodiment, it can be realized by entering identifical inputs to the GF(2^(e)) multiplication device 21 of FIG. 8.

FIG. 9 shows a configuration of a GF(2^(e)) inverse calculation device 22 to be used for arithmetic operations in finite field according to this second embodiment. This GF(2^(e)) inverse calculation device 22 comprises a query unit 22 a and a query unit 22 a and an inverse table 22 b, which are configured to operate as follows.

STEP 301: The query unit 22 a looks up the inverse table 22 b by using an input x as retrieval key so as to retrieve an inverse x⁻¹ of the input x.

STEP 302: The query unit 22 a outputs a value x-obtained from the inverse table 22 b.

FIG. 10 shows a configuration of a GF(2² ) addition device 23 according to this second embodiment which realizes calculation of the above described equation (1). This GF(2^(2n)) addition device 23 comprises two GF(2^(n)) addition devices 23 a and 23 b, which are configured to operate as follows.

STEP 401: The GF(2^(n)) addition device 23 a calculates a sum x₃ of x₁ in one input m₁=x₁+y₁ α and x₂ in another input m₂=x₂+y₂α.

STEP 402: The GF(2^(n)) addition device 23 b calculates a sum y₃ of y₁ in the input m₁=x₁+y₁ a and y₂ in the input m₂=x₂+y₂α.

STEP 403: A set of x₃ and y₃ that gives a sum m₃=m₁+m₂=x₃+y₃α is outputted.

FIG. 11 shows a configuration of a GF(2^(2n)) multiplication device 24 according to this second embodiment which realizes calculation of the above described equation (2). This GF(2^(2n)) multiplication device 24 comprises four GF(2^(n)) addition devices (add) 24 a, 24 c, 24 f and 24 h and four GF(2^(n)) multiplication devices (mul) 24 b, 24 d, 24 e and 24 g, which are configured to operate as follows.

STEP 501: The add device 24 a calculates a sum t₁ of inputs x₁ and y₁, and outputs this t₁ to the mul device 24 e.

STEP 502: The mul device 24 b calculates a product t₂ of inputs x₁ and y₁, and outputs this t₂ to the add device 24 f.

STEP 503: The add device 24 c calculates a sum ta of inputs x₂ and y₂, and outputs this t₃ to the mul device 24 e.

STEP 504: The mul device 24 d calculates a product t₄ of inputs x₂ and y₂, and outputs this t₄ to the add device 24 h.

STEP 505: The mul device 24e calculates a product t₅ of inputs t₁ and t₃, and outputs this t₅ to the add device 24 f and the mul device 24 g.

STEP 506: The add device 24 f calculates a sum y₃ of inputs t₂ and t₅, and outputs this y₃ as part of the output of the GF(2^(2n)) multiplication device 24.

STEP 507: The mul device 24 g calculates a product t₇ of an input t₅ and a constant a, and outputs this t₇ to the add device 24 h.

STEP 508: The add device 24h calculates a sum x₃ of inputs t₄ and t₇, and outputs this x₃ as part of the output of the GF=(2^(2n)) multiplication device 24.

Thus the GF(2^(2n)) multiplication device 24 outputs a set x₃ and y₃ that gives a product m₃=m₁×m₂=x₃+y₂α.

FIG. 12 shows a configuration of a GF(2^(2n)) square calculation device 25 according to this second embodiment which realizes calculation of the above described equation (3). This GF(2^(2n)) square calculation device 25 comprises one GF(2^(n)) addition device (add) 25 d, one GF(2^(n)) multiplication device (mul) 25c, and two GF(2^(n)) square calculation devices (sqr) 25a and 25b, which are configured to operate as follows.

STEP 601: The sqr device 25 a calculates a square t₁ of an input x₁, and outputs this ti to the add device 25 d.

STEP 602: The sqr device 25 b calculates a square t₂ of an input y₁, and outputs this t₂ to the mul device 25 c.

STEP 603: The mul device 25 c calculates a product y₃ of an input t₂ and a constant a, and outputs this y₃ to the add device 25 d while also outputting this y₃ as part of the output of to the square calculation device 25.

STEP 604: The add device 25 d calculates a sum x₃ of the inputs t₁ and y₃, and outputs this x₃ as part of the output of the square calculation device 25.

Thus the GF(2^(2n)) square calculation device 25 outputs a set x₃ and y₃ that gives a square m₁ ²=x₃+y₃α.

FIG. 13 shows a configuration of a GF(2^(2n)) inverse calculation device 26 according to this second embodiment which realizes calculation of the above described equation (4). This GF(2^(2n)) inverse calculation device 26 comprises two GF(2^(n)) addition devices (add) 26 a and 26 e, four GF(2^(n)) multiplication devices (mul) 26 b, 26 d, 26 g and 26 h, one GF(2^(n)) square calculation device (sqr) 26 c, and one GF(2^(n)) inverse calculation device (inv) 26 f, which are configured to operate as follows.

STEP 701: The add device 26 a calculates a sum t₁ of inputs x₁ and y₁, and outputs this t₁ to the mul device 26 b and the mul device 26 g.

STEP 702: The mul device 26 b calculates a product t₂ of inputs x₁ and t₁, and outputs this t₂ to the add device 26 e.

STEP 703: The sqr device 26 c calculates a square t₃ of an input y₁, and outputs this t₃ to the mul device 26 d.

STEP 704: The mul device 26d calculates a product t₄ of an input t₃ and a constant a, and outputs this t₄ to the add device 26 e.

STEP 705: The add device 26 e calculates a sum t₅ of inputs t₂ and t₄,l and outputs this t₅ to the inv device 26 f.

STEP 706: The inv device 26 f calculates an inverse t₆ of an input t₅, and outputs this t₆ to the mul device 26 g and the mul device 26 h.

STEP 707: The mul device 26 g calculates a product x₃ of inputs t₁ and t₆, and outputs this x₃ as part of the output of the GF(2^(2n)) inverse calculation device 26.

STEP 708: The mul device 26 h calculates a product y₃ of inputs y₁ and t₆, and outputs this y₃ as part of the output of the GF(2^(2n)) multiplication device 24.

Thus the GF(2^(2n)) inverse calculation device 26 outputs a set x₃ and y₃ that gives an inverse m₁ ⁻¹=x₃+y₃α.

FIG. 14 shows a comparison of finite field arithmetic operation performances by this second embodiment and the first embodiment described above.

As can be seen in FIG. 14, this second embodiment is superior to the first embodiment in terms of depth, for all the cases except for the addition. In addition, this second embodiment is superior to the first embodiment in terms of. a required number of additions for the square calculation. Since the inverse calculation internally uses the square calculation, this second embodiment can be implemented to be faster than the first embodiment for the inverse calculation as well.

It is to be noted that, when operations in finite field are to be reduced to operations in its subfield successively in a sequence of:

GF(2^(e2) ^(t) )→GF(2^(e2) ^(t−1) )→ . . . →GF(2^(e))

it is possible to use the scheme of the first embodiment in one reduction stage and the scheme of the second embodiment in another reduction stage. For instance, it is possible to use the scheme of the first embodiment for the reduction of operations in GF(2^(e2) ^(t) ) to operations in GF(2^(e2) ^(t−1) ), while using the scheme of the second embodiment for the reduction of operations in GF(2^(e2) ^(t−1) ) to operations in GF(2^(e2) ^(t−2) ), and so on so forth.

Referring now to FIG. 15 to FIG. 21, the third embodiment of a scheme for group operations over elliptic curves according to the present invention will be described in detail. This third embodiment is an application of a scheme for arithmetic operations in finite field of the first or second embodiment described above to group operations over elliptic curves in finite field.

When appropriate field K is defined, group E(K) over elliptic curves can be expressed as:

E(K)={(x, y)∈K ² |f(x, y)=0}U{0}

where f(x, y)∈K[x, y] (but f(x, y) cannot be chosen arbitrarily and is subjected to some constraints). For P_(i)=E(K) (i=1, 2, 3), assuming that P₃=P₁+P₂ holds when P_(i)≠0 (i=1, 2, 3), group operations over elliptic curves can be expressed in terms of appropriate polynomials p(x₁, x₂, y₂, y₂), q(x₁, x₂, y₁, y₂), r(x₁, x₂, y₁, y₂), s(x₁, x₂, y₁, y₂)∈K[x ₁, x₂, y₁, y₂] as follows. $\left\{ \begin{matrix} {x_{3} = \frac{p\left( {x_{1},x_{2},y_{1},y_{2}} \right)}{q\left( {x_{1},x_{2},y_{1},y_{2}} \right)}} \\ {y_{3} = \frac{r\left( {x_{1},x_{2},y_{1},y_{2}} \right)}{s\left( {x_{1},x_{2},y_{1},y_{2}} \right)}} \end{matrix} \right.$

Note here that polynomials p, q, r, s are determined and not dependent on P_(i) when the elliptic curves E(K) are fixed, but polynomials p, q, r, s are different for a case of P₁=P₂ and a case of P₁≠P₂. From the above it can be seen that group operations over elliptic curves can be constructed from arithmetic operations, in field K.

Now this third embodiment will be described in further detail with references to the drawings.

Non-supersingular elliptic curves over GF(2^(e2) ^(t) ) can be defined in terms of parameters:

a ₂ , a ₆ ∈GF(2_(e2) ^(t))(a ₆≠0)

by using the affine coordinates as follows.

E(GF(2^(e2) ^(t) ))={(x, y)∈GF(2^(e2) ^(t) )²|

y ² +xy=x ³ +a ₂ x ² +a ₆ }U{0}

In this case, the addition over the elliptic curves is defined such that, when:

P _(i)=(x _(i) , y _(i))∈E(GF(2^(e2) ^(t) ))(i=1, 2)

assuming that −P₁≠P₂, (x₃, y₃)=P₁+P₂ can be given by: $\begin{matrix} {{{{When}\quad P_{1}} \neq P_{2}},\quad {{{setting}\quad \lambda} = \frac{y_{1} + y_{2}}{x_{1} + x_{2}}},\left\{ \begin{matrix} {x_{3} = {\lambda^{2} + \lambda + \left( {x_{1} + x_{2}} \right) + a_{2}}} \\ {y_{3} = {{\lambda \left( {x_{1} + x_{3}} \right)} + x_{3} + y_{1}}} \end{matrix} \right.} & (5) \\ {{{{{When}\quad P_{1}} = P_{2}},\quad {{{setting}\quad \lambda} = {x_{1} + \frac{y_{1}}{x_{1}}}}}\left\{ \begin{matrix} {x_{3} = {\lambda^{2} + \lambda + a_{2}}} \\ {y_{3} = {{\left( {\lambda + 1} \right)x_{3}} + x_{1}^{2}}} \end{matrix} \right.} & (6) \end{matrix}$

Also, the inverse over the elliptic curves can be expressed as follows.

−(x ₁ , y ₁)=(x ₁ , x ₁ +y ₁)  (7)

(For details of group operations over elliptic curves in field with characteristic 2, see A. J. Menezes: “Elliptic Curve Public Key Cryptosystems”, Kluwer Academic Publishers, pp. 21-23, 1993, for example.)

An element in finite field GF(2^(e2) ^(t) ) can be expressed by a bit sequence in e2^(t) digits, and a point on an elliptic curve can be expressed by two elements in finite field, so that a point on an elliptic curve can be expressed by a bit sequence in 2e2^(t) bits. In the following, a point P_(i) on an elliptic curve is assumed to be expressed in this way. Note however that 0∈E(GF(2^(e2) ^(t) )) can be expressed as 0=(0, 0) because:

(0, 0)∉E(GF(2^(e2) ^(t) ))

FIG. 15 shows a configuration of an elliptic curve group inverse calculation device 1 for an element of a group over elliptic curves according to this third embodiment which realizes calculation of the above equation (7). This elliptic curve group inverse calculation device 1 comprises a query unit la and a finite field addition device 1 b such as that shown in FIG. 10 described above, which are configured to operate as follows.

STEP 801: The query unit 1 a gives input (x, y) to the finite field addition device 1 b.

STEP 802: The finite field addition device lb calculates a sum x+y in GF(2^(e2) ^(t) ) of the input x and y, and returns it to the query unit 1 a.

STEP 803: The query unit la outputs an inverse (x, x+y) of an element of a group over elliptic curves by using x+y obtained from the finite field addition device 1 b.

FIG. 16 shows an elliptic curve group addition device 3 for elements of a group over elliptic curves according to this third embodiment which realizes calculation of the above equation (5). This elliptic curve group addition device 3 comprises a query unit 3 a, an elliptic curve group inverse calculation device 3 b for an element of a group over elliptic curves such as that shown in FIG. 15 described above, an elliptic curve group comparison device 3 c for elements of a group over elliptic curves such as that of FIG. 17 to be described below, and an elliptic curve group special addition device 3 d for elements of a group over elliptic curves such as that of FIG. 18 to be described below, which are configured to operate as follows.

STEP 901: The query unit 3 a checks whether P₁=0 or not by using the elliptic curve group comparison device 3 c, and if it is TRUE, the query unit 3 a sets Q=P₂ and proceeds to the step 908.

STEP 902: The query unit 3 a checks whether P₂=0 or not by using the elliptic curve group comparison device 3 c, and if it is TRUE, the query unit 3 a sets Q=P₁ and proceeds to the step 908.

STEP 903: The query unit 3 a gives an input P₁ to the elliptic curve group inverse calculation device 3 b.

STEP 904: The elliptic curve group inverse calculation device 3 b calculates an inverse −P₁ of the input P₁, and returns it to the query unit 3 a.

STEP 905: The query unit 3 a gives the output −P₁ of the elliptic curve group inverse calculation device 3 b and its own input P₂ to the elliptic curve group comparison device 3 c.

STEP 906: The elliptic curve group comparison device 3 c compares inputs −P₁ and P₂, and returns T (TRUE) when they coincide or F (FALSE) otherwise.

STEP 907: The query unit 3 a sets Q=0 when the output of the elliptic curve group comparison device 3 c at the step 906 is T (TRUE), or obtains Q=P₁+P₂ by giving Pi and P₂ to the elliptic curve group special addition device 3 d otherwise.

STEP 908: The query unit 3 a outputs Q (=P₁+P₂).

FIG. 17 shows an elliptic curve group comparison device 4 for elements of a group over elliptic curves according to this third embodiment which can be used in the elliptic curve group addition device of FIG. 16 described above. This elliptic curve group comparison device 4 comprises a query unit 4 a and a 2e2^(t) bit sequence comparison unit 4 b, which are configured to operate as follows.

STEP 1001: The query unit 4 a gives bit sequences x₁||y₁ and x₂||y₂ obtained by Joining bit sequences x₁ and y₁ of an input P₁=(x₁, y₁) and bit sequences x₂ and y₂ of an input P₂=(x₂, y₂), respectively, to 2e2^(t) bit sequence comparison unit 4 b.

STEP 1002: The 2e2^(t) bit sequence comparison unit 4 b compares x₁||y₁ and x₂||y₂, and returns T (TRUE) when they coincide or F (FALSE) otherwise.

STEP 1003: The query unit 4 a outputs T/F value obtained by the 2e2^(t) bit sequence comparison unit 4 b.

FIG. 18 shows an elliptic curve group special addition device 5 for elements of a group over elliptic curves according to this third embodiment which can be used in the elliptic curve group addition device of FIG. 16 described above. This elliptic curve group special addition device 5 comprises eight finite field addition devices (add) 5 a, 5 c, 5 f, 5 g, 5 h, 5 i, 5 k and 5 l, one finite field inverse calculation device 5 b, two finite field multiplication devices (mul) 5 d and 5 j, and one finite field square calculation device (sqr) 5 e, which are-configured to operate as follows.

STEP 1101: The add device 5 a calculates a sum t₁(=x₁+x₂ ) of inputs x₁ and x₂, and outputs this t₁ to the inv device 5 b and the add device 5 g.

STEP 1102: The inv device 5 b calculates an inverse t₂ (=(x₁+x₂)⁻¹) of an input t₁, and outputs this t₂ to the mul device 5 d.

STEP 1103: The add device 5 c calculates a sum t₃ (=y₁+y₂) of inputs y₁ and y₂, and outputs this t₃ to the mul device 5 d.

STEP 1104: The mul device 5 d calculates a product: $\lambda \left( {= \frac{y_{1} + y_{2}}{x_{1} + x_{2}}} \right)$

of inputs t₂ and t_(3,) and outputs this X to the sqr device 5 e, the add device 5 f, and the mul device 5 j.

STEP 1105: The sqr device 5 e calculates a square t₄ (=λ²) of an input λ, and outputs this t₄ to the add device 5 f.

STEP 1106: The add device 5 f calculates a sum t₅ (=λ²+λ) of inputs λ and t₄, and outputs this t₅ to the add device 5 g.

STEP 1107: The add device 5 g calculates a sum t₆ (=λ²+x+x₁+x₂) of inputs t₁ and t₅, and outputs this t₆ to the add device 5 h.

STEP 1108: The add device 5 h calculates a sum x₃ (=λ²+λ+x₁+x₂+a₂) of an input t₆ and a constant a₂, and outputs this x₃ to the add device 5 i and the add device 5 k while also outputting this x₃ as part of the output of the elliptic curve group special addition device 5.

STEP 1109: The add device 5 i calculates a sum t₇ (=x +x₃) of inputs x₁ and x₃, and outputs this t₇ to the muI device 5 j.

STEP 1110: The mul device 5 j calculates a product t₈ (=λ(x₁+x₃)) of inputs λ and t₇, and outputs this t₈ to the add device 5 k.

STEP 1111: The add device 5 k calculates a sum t₉ (=λ(x₁+x₃)+x₃) of inputs t₈ and x₃, and outputs this t₉ to the add device 51.

STEP 1112: The add device 51 calculates a sum y₃ (=λ(x₁+x₃)+x₃+y₁) of inputs y₁ and t₉, and outputs this y₃ as part of the output of the elliptic curve group special addition device 5.

Note that each finite field multiplication device (mul) used in this elliptic curve group special addition device 5 can have a configuration as shown in FIG. 11 described above. The finite field square calculation device (sqr) used in this elliptic curve group special addition device 5 can be realized by a configuration of FIG. 11 described above, but can be realized in faster implementation by a special configuration for square calculation such as that of FIG. 12 described above. The finite field inverse calculation device (inv) used in this elliptic curve group special addition device 5 can have a configuration as shown in FIG. 13 described above.

FIG. 19 shows an elliptic curve group double calculation device 10 for an element of a group over elliptic curves according to this third embodiment which realizes calculation of the above equation (6). This elliptic curve group double calculation device 10 comprises a query unit 10 a and an elluiptic curve group special double calculation device 10 b for an element of a group over elliptic curves such as that of FIG. 20 to be described below, which are configured to operate as follows.

STEP 1201: The query unit 10 a sets Q=0 when x=0 in an input P=(x, y), or obtains Q=2P by giving the input P to the elliptic curve group special double calculation device 10 b otherwise.

STEP 1202: The query unit 10 a outputs Q (=2P).

FIG. 20 shows an elliptic curve group special double calculation device 11 for an element of a group over elliptic curves according to this third embodiment which can be used in the elliptic curve group double calculation device of FIG. 19 described above. This elliptic curve group special double calculation device 11 comprises five finite field addition devices (add) 11 d, 11 f, 11 g, 11 h and 11 j, one finite field inverse calculation device 11 b, two finite field multiplication devices (mul) 11 c and 11 i, and two finite field square calculation devices (sqr) 11 a and 11 e, which are configured to operate as follows.

STEP 1301: The sqr device hla calculates a square t₁ (=x²) of an input x, and outputs this t₁ to the add device 11 j.

STEP 1302: The inv device 11 b calculates an inverse ts (=x⁻¹) of the input x, and outputs this t₂ to the mul device 11 c.

STEP 1303: The mul device 11 c calculates a product t₂ (=x⁻¹×y) of inputs y and t₂, and outputs this t₃ to the add device 11 d.

STEP 1304: The add device 11 d calculates a sum: $\lambda \left( {= {x + \frac{y}{x}}} \right)$

of inputs x and y₃, and outputs this λ to the sqr device 11 e, the add device 11 f and the add device 11 h.

STEP 1305: The sqr device 11 e calculates a square t₄ (=λ²) of an input λ, and outputs this t₄ to the add device 11 f.

STEP 1306: The add device 11 f calculates a sum t₅ (=λ²+λ) of inputs λand t₄, and outputs-this t₅ to the add device 11 g.

STEP 1307: The add device 11 g calculates a sum x₂(=λ²+λ+a₂) of an input t₅ and a constant a₂, and outputs this x₃ as part of the output of the elliptic curve group special double calculation device 11.

STEP 1308: The add device 11 h calculates a sum t₆ (=λ+1) of an input λ and a constant 1, and outputs this t₆ to the mul device 11 i.

STEP 1309: The mul device 11 calculates a product t₇ (=(λ+1)x₃) of inputs x₃ and t₆, and outputs this t₇ to the add device 11 j.

STEP 1310: The add device 11 j calculates a sum y₃ (=(λ+1)x₃+x₁ ²) of inputs t₁ and t₇, and outputs this y₃ as part of the output of the elliptic curve group special double calculation device 11.

FIG. 21 shows an elliptic curve group natural number multiple calculation device 12 for an element of a group over elliptic curves according to this third embodiment. A natural number multiple of an element of a group over elliptic curves can be realized in various ways, and FIG. 21 shows an implementation using a binary calculation method (see B. Schneier, Applied Cryptography, 2nd Edition, pp. 242-244). This elliptic curve group natural number multiple calculation device 12 comprises a control unit 12 a, an elliptic curve group comparison device 12 b for elements of a group over elliptic curves such as that shown in FIG. 17 described above, an elliptic curve group addition device 12 c for elements of a group over elliptic curves such as that shown in FIG. 16 described above, and an elliptic curve group double calculation device 12 d for an element of a group over elliptic curves such as that shown in FIG. 19 described above, which are configured to operate as follows.

STEP 1401: The control unit 12 a initializes internal variables Q and R as follows.

Q=0

R=P

STEP 1402: The control unit 12 a checks a value of n, and outputs Q as the output of the elliptic curve group natural number multiple calculation device 12 and then stops operating when n coincides with 0.

STEP 1403: The control unit 12 a checks a value of n, and sets:

n←n−1

when n is odd. Then, control unit 12 a compares values of Q and R by using the elliptic curve group comparison device 12 b, and calculates:

Q←Q+R

by using the elliptic curve group double calculation device 12 d when Q and R coincide, or by using the elliptic curve group addition device 12 c when Q and R do not coincide.

STEP 1404: The control unit 12 a calculates:

n←n/2

STEP 1405: The control unit 12 a calculates:

R←2R

by using the elliptic curve group double calculation device 12 d.

STEP 1406: The control unit 12 a returns the operation back to the step 1402 described above.

Now, this scheme for group operations over elliptic curves according to the third embodiment can be applied to various fields such as those of cipher communications and electronic money. Here, the key sharing, encryption, and digital signing in a case of applying this third embodiment to the fields of cipher communications and electronic money will be described.

First, the Diffie-Hellman key sharing scheme using elliptic curves will be described. Here, system parameters are assumed to be an elliptic curve E(GF(2^(n))) and an element P∈E(GF(2^(n))) of large order.

In this case, at a time of key generation, a user U randomly generates a positive integer xu and calculates:

Y _(U) =x _(U) P

where x_(U) is a secret key and Y_(U) is a public key.

Next, the key sharing between users A and B can be realized as follows.

STEP 1501: The user A acquires the public key Y_(B) of the user B somehow.

STEP 1502: The user A calculates:

K _(A,B) =x _(A) Y _(B)

STEP 1503: The user B similarly calculates:

As a result, the key K_(A,B)=K_(B,A) is shared between the users A and B.

By applying the scheme for group operations over elliptic curves of the third embodiment to this procedure it becomes possible to realize the faster processing speed.

Next, the ElGamal encryption using elliptic curves will be described. Here, system parameters are assumed to be an elliptic curve E(GF(2^(n))) and an element P∈E(GF(2^(n))) of large order.

In this case, at a time of key generation, a user U randomly generates a positive integer x_(U) and calculates:

Y _(U) =x _(U) P

where x_(U) is a secret key and Y_(U) is a public key.

Next, a sender can encrypt a plaintext M and transmits a resulting ciphertext to a receiver user A as follows.

STEP 1601: The sender acquires the public key Y_(A) of the user A somehow.

STEP 1602: The sender generates a positive integer random number r.

STEP 1603: The sender calculates the ciphertext (C₁, C₂) as follows.

C ₁ =rP

C ₂ =M+rY _(A)

STEP 1604: The user A can obtain the plaintext M by carrying out the decryption of the ciphertext (C₁, C₂) as follows.

M=C ₂ −x _(A) C ₁

Next, the ElGamal digital signature using elliptic curves will be described. Here, system parameters are assumed to be an elliptic curve E(GF(2^(n))), an element P∈E(GF(2^(n))) of large order, and an order ^(#)E(GF(2^(n))) of the elliptic curve, and a one-way hash function h is to be used.

In this case, at a time of key generation, a user U randomly generates a positive integer x_(U) and calculates:

Y _(U) =x _(U) P

where x_(U) is a secret key and Y_(U) is a public key.

Next, a user A can digitally sign data m as follows.

STEP 1701: The user A randomly selects a positive integer k which is relatively prime with respect to ^(#)E(GF(2^(n))).

STEP 1702: The user A calculates the signature (R, s) as follows.

R=kP

s=(m−x _(A) h(r))k ⁻¹mod ^(#) E(GF(2^(n)))

Then, the authenticity of this signature (R, s) can be verified as follows.

mP=h(R)Y _(A) +R

It is to be noted that, according to this scheme of the third embodiment, when elements other than a point at infinity 0 of a group over elliptic curves E(GF(2^(2n))) in finite field GF(2^(2n)) are expressed in terms of affine coordinates (x, y), group operations over elliptic curves can be expressed as rational expressions in x and y, and it is possible to calculate a rational expression in a form of: $\frac{p\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}{q\left( {X_{1},X_{2},\cdots \quad,X_{r}} \right)}$

where p, q∈GF(2^(2n))([X₁, X₂, . . . , Xr]

by calculating a multiplication in GF(2^(2n)) that arises in calculations using polynomials p, q∈GF(2^(2n))[X₁, X₂, Xr] according to:

(x ₁ +y ₁α)×(x ₂ +y ₂α)

=(x ₁ x ₂ +ay ₁ y ₂+((x ₁ +y ₁)(x ₂ +y ₂)+x ₁ x ₂)α

and

(x ₁ +y ₁α)²=(x ₁ ² +ay ₁ ²)+y ₁ ²α

where x₁, x₂, y₁, y₂, a∈GF(2^(n)), α∈GF(2^(n)), α²+α+α=0 and GF(2^(2n))=GF(2^(n))(α);

and calculating an inverse q⁻¹ of q in GF(2^(2n)) according to:

(x _(i) +y ₁α)⁻¹=(x ₁(x ₁ +y ₁)+ay ₁ ²)⁻¹((x ₁ +y ₁)+y ₁α)

where x₁, y₁, a∈GF(2^(n)), α∉GF(2^(n)), α²+α+a=0 and GF(2^(2n))=GF(2^(n))(α);

and then calculating p×q⁻¹ by repeating the multiplication as described above for calculations using polynomials p and q.

It is to be noted that the above described embodiments according to the present invention may be conveniently implemented using conventional general purpose digital computers programmed according to the teachings of the present specification, as will be apparent to those skilled in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art.

In particular, any of the devices shown in FIG. 1 to FIG. 13 and FIG. 15 to FIG. 21 described above can be conveniently implemented in forms of software package.

Such a software package can be a computer program product which employs a storage medium including stored computer code which is used to program a computer to perform the disclosed function and process of the present invention. The storage medium may include, but is not limited to, any type of conventional floppy disks, optical disks, CD-ROMS, magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, or any other suitable media for storing electronic instructions.

It is also to be noted that, besides those already mentioned above, many modifications and variations of the above embodiments may be made without departing from the novel and advantageous features of the present invention. Accordingly, all such modifications and variations are intended to be included within the scope of the appended claims. 

What is claimed is:
 1. A method for calculating a square in finite field GF(2^(2n)), comprising the steps of: expressing an element m∈GF(2^(2n))≅GF(2^(n))[x]/(x²+x+a) as m=x+yα(x, y∈GF(2^(n))) where α∉GF(2^(n)), α²+α+a=0, and a∈GF(2^(n)) so that a square m² of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications and additions in subfield GF(2^(n)) given by m ²=(x ² +ay ²)+y ²α by using a standard basis [1 α]; and calculating the square m² of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications and additions in the subfield GF(2^(n)).
 2. A device for calculating a square in finite field GF(2^(2n)), comprising: an input unit for expressing an element m∈GF(2^(2n))≅GF(2^(2n))[x]/(x²+x+a) as m=x+yα(x, y∈GF(2^(n))) where α∉GF(2^(n)), α²+α+a=0, and a∈GF(2^(n)) so that a square m² of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications and additions in subfield GF(2^(n)) given by m ²(x ² +ay ²)+y ²α by using a standard basis [1 α]; and a calculation unit for calculating the square m² of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications and additions in the subfield GF(2^(n)).
 3. An article of manufacture, comprising: a computer usable medium having computer readable program code means embodied therein for causing a computer to function as a system for calculating a square in finite field GF(2^(2n)), the computer readable program code means includes: first computer readable program code means for causing said computer to express an element m∈GF(2^(2n)) GF(2^(n))[x]/(x²+x+a) as m=x+yα(x, y∈GF(2^(n))) where α∈GF(2^(n)), α²+α+a=0, and a∈GF(2^(n)) so that a square m² of the element m in the finite field GF(2^(2n)) is expressed as a combination of multiplications and additions in subfield GF(2^(n)) given by m ²=(x ² +ay ²)+y ²α by using a standard basis [1 α]; and second computer readable program code means for causing said computer to calculate the square m² of the element m in the finite field GF(2^(2n)) by executing said combination of multiplications and additions in the subfield GF(2^(n)). 